
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    :   str						=			logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   SwinConfig.from_pretrained(
							    "microsoft/swin-tiny-patch4-window7-224"		,    out_features=["stage1", "stage2", "stage3", "stage4"])
							__UpperCamelCase      :  Dict       =   MaskFormerConfig(backbone_config=_lowerCamelCase)

							__UpperCamelCase      :  Optional[int]       =   "huggingface/label-files"
							if "ade20k-full" in model_name:
														# this should be ok
														__UpperCamelCase      :  str       =   847
														__UpperCamelCase      :  List[Any]       =   "maskformer-ade20k-full-id2label.json"
							elif "ade" in model_name:
														# this should be ok
														__UpperCamelCase      :  Optional[Any]       =   150
														__UpperCamelCase      :  Any       =   "ade20k-id2label.json"
							elif "coco-stuff" in model_name:
														# this should be ok
														__UpperCamelCase      :  List[Any]       =   171
														__UpperCamelCase      :  List[Any]       =   "maskformer-coco-stuff-id2label.json"
							elif "coco" in model_name:
														# TODO
														__UpperCamelCase      :  int       =   133
														__UpperCamelCase      :  int       =   "coco-panoptic-id2label.json"
							elif "cityscapes" in model_name:
														# this should be ok
														__UpperCamelCase      :  Optional[Any]       =   19
														__UpperCamelCase      :  str       =   "cityscapes-id2label.json"
							elif "vistas" in model_name:
														# this should be ok
														__UpperCamelCase      :  Tuple       =   65
														__UpperCamelCase      :  Dict       =   "mapillary-vistas-id2label.json"

							__UpperCamelCase      :  Optional[int]       =   json.load(open(hf_hub_download(_lowerCamelCase		,    _lowerCamelCase		,    repo_type="dataset")		,    "r"))
							__UpperCamelCase      :  List[Any]       =   {int(_lowerCamelCase): v for k, v in idalabel.items()}

							return config




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str])   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   []
							# stem
							# fmt: off
							rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight"))
							rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias"))
							rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight"))
							rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias"))
							# stages
							for i in range(len(config.backbone_config.depths)):
														for j in range(config.backbone_config.depths[i]):
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight'))
																					rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias'))

														if i < 3:
																					rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight'))
																					rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight'))
																					rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias'))
														rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight'))
														rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias'))

							# FPN
							rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight"))
							rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight"))
							rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias"))
							for source_index, target_index in zip(range(3		,    0		,    -1)		,    range(0		,    3)):
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight'))
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight'))
														rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias'))
														rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight'))
														rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight'))
														rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias'))
							rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight"))
							rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias"))

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers):
														# self-attention out projection
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias'))
														# cross-attention out projection
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias'))
														# MLP 1
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias'))
														# MLP 2
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias'))
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias'))
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias'))
														# layernorm 3 (final layernorm)
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight'))
														rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias'))

							rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight"))
							rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias"))

							# heads on top
							rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight"))

							rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight"))
							rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias"))

							rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight"))
							rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias"))

							for i in range(3):
														rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight'))
														rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias'))
							# fmt: on

							return rename_keys




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     List[str])   ->       Tuple:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   dct.pop(_lowerCamelCase)
							__UpperCamelCase      :  int       =   val




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     Dict)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
							for i in range(len(backbone_config.depths)):
														__UpperCamelCase      :  Any       =   num_features[i]
														for j in range(backbone_config.depths[i]):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					__UpperCamelCase      :  Union[str, Any]       =   state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight')
																					__UpperCamelCase      :  List[str]       =   state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias')
																					# next, add query, keys and values (in that order) to the state dict
																					__UpperCamelCase      :  int       =   in_proj_weight[:dim, :]
																					__UpperCamelCase      :  List[str]       =   in_proj_bias[: dim]
																					__UpperCamelCase      :  List[str]       =   in_proj_weight[
																					    dim : dim * 2, :
																					]
																					__UpperCamelCase      :  Tuple       =   in_proj_bias[
																					    dim : dim * 2
																					]
																					__UpperCamelCase      :  List[Any]       =   in_proj_weight[
																					    -dim :, :
																					]
																					__UpperCamelCase      :  Optional[Any]       =   in_proj_bias[-dim :]
																					# fmt: on




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Dict		,    _lowerCamelCase					:     int)   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  Tuple       =   config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														__UpperCamelCase      :  str       =   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight')
														__UpperCamelCase      :  List[Any]       =   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias')
														# next, add query, keys and values (in that order) to the state dict
														__UpperCamelCase      :  Optional[Any]       =   in_proj_weight[: hidden_size, :]
														__UpperCamelCase      :  int       =   in_proj_bias[:config.hidden_size]
														__UpperCamelCase      :  Any       =   in_proj_weight[hidden_size : hidden_size * 2, :]
														__UpperCamelCase      :  Optional[int]       =   in_proj_bias[hidden_size : hidden_size * 2]
														__UpperCamelCase      :  Any       =   in_proj_weight[-hidden_size :, :]
														__UpperCamelCase      :  Optional[Any]       =   in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														__UpperCamelCase      :  Union[str, Any]       =   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight')
														__UpperCamelCase      :  str       =   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias')
														# next, add query, keys and values (in that order) to the state dict
														__UpperCamelCase      :  str       =   in_proj_weight[: hidden_size, :]
														__UpperCamelCase      :  Optional[int]       =   in_proj_bias[:config.hidden_size]
														__UpperCamelCase      :  List[str]       =   in_proj_weight[hidden_size : hidden_size * 2, :]
														__UpperCamelCase      :  Dict       =   in_proj_bias[hidden_size : hidden_size * 2]
														__UpperCamelCase      :  Any       =   in_proj_weight[-hidden_size :, :]
														__UpperCamelCase      :  int       =   in_proj_bias[-hidden_size :]
							# fmt: on




def       _SCREAMING_SNAKE_CASE   (						)   ->       torch.Tensor:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase      :  Optional[Any]       =   Image.open(requests.get(_lowerCamelCase		,    stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     bool = False)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   get_maskformer_config(_lowerCamelCase)

							# load original state_dict
							with open(_lowerCamelCase		,    "rb") as f:
														__UpperCamelCase      :  str       =   pickle.load(_lowerCamelCase)
							__UpperCamelCase      :  Union[str, Any]       =   data["model"]

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							__UpperCamelCase      :  Dict       =   create_rename_keys(_lowerCamelCase)
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
							read_in_swin_q_k_v(_lowerCamelCase		,    config.backbone_config)
							read_in_decoder_q_k_v(_lowerCamelCase		,    _lowerCamelCase)

							# update to torch tensors
							for key, value in state_dict.items():
														__UpperCamelCase      :  Dict       =   torch.from_numpy(_lowerCamelCase)

							# load 🤗 model
							__UpperCamelCase      :  List[Any]       =   MaskFormerForInstanceSegmentation(_lowerCamelCase)
							model.eval()

							for name, param in model.named_parameters():
														print(_lowerCamelCase		,    param.shape)

							__UpperCamelCase  ,		__UpperCamelCase      :  Optional[Any]       =   model.load_state_dict(_lowerCamelCase		,    strict=_lowerCamelCase)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(_lowerCamelCase) == 0, F'Unexpected keys: {unexpected_keys}'

							# verify results
							__UpperCamelCase      :  Any       =   prepare_img()
							if "vistas" in model_name:
														__UpperCamelCase      :  str       =   65
							elif "cityscapes" in model_name:
														__UpperCamelCase      :  Any       =   65_535
							else:
														__UpperCamelCase      :  Any       =   255
							__UpperCamelCase      :  List[str]       =   True if "ade" in model_name else False
							__UpperCamelCase      :  Dict       =   MaskFormerImageProcessor(ignore_index=_lowerCamelCase		,    reduce_labels=_lowerCamelCase)

							__UpperCamelCase      :  Optional[Any]       =   image_processor(_lowerCamelCase		,    return_tensors="pt")

							__UpperCamelCase      :  Union[str, Any]       =   model(**_lowerCamelCase)

							print("Logits:"		,    outputs.class_queries_logits[0, :3, :3])

							if model_name == "maskformer-swin-tiny-ade":
														__UpperCamelCase      :  Tuple       =   torch.tensor(
														    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]])
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,    _lowerCamelCase		,    atol=1e-4)
							print("Looks ok!")

							if pytorch_dump_folder_path is not None:
														print(F'Saving model and image processor to {pytorch_dump_folder_path}')
														Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
														model.save_pretrained(_lowerCamelCase)
														image_processor.save_pretrained(_lowerCamelCase)

							if push_to_hub:
														print("Pushing model and image processor to the hub...")
														model.push_to_hub(F'nielsr/{model_name}')
														image_processor.push_to_hub(F'nielsr/{model_name}')


if __name__ == "__main__":
		lowercase    :   int						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='maskformer-swin-tiny-ade',
		    type=str,
		    help=('Name of the MaskFormer model you\'d like to convert',),
		)
		parser.add_argument(
		    '--checkpoint_path',
		    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
		    type=str,
		    help='Path to the original state dict (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		lowercase    :   Tuple						=			parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)

from __future__ import annotations




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:str	,  a						:str		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   text, pattern
								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   len(a		), len(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if char == self.pattern[i]:
																						return i
								return -1







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if self.pattern[i] != self.text[current_pos + i]:
																						return current_pos + i
								return -1







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					list[int]:
								# searches pattern in text and returns index positions
								__UpperCamelCase      :  Any       =   []
								for i in range(self.textLen - self.patLen + 1		):
															__UpperCamelCase      :  List[Any]       =   self.mismatch_in_text(a		)
															if mismatch_index == -1:
																						positions.append(a		)
															else:
																						__UpperCamelCase      :  Any       =   self.match_in_pattern(self.text[mismatch_index]		)
																						__UpperCamelCase      :  Dict       =   (
																						    mismatch_index - match_index
																						)  # shifting index lgtm [py/multiple-definition]
								return positions
lowercase    :   Any						=			'ABAABA'
lowercase    :   str						=			'AB'
lowercase    :   str						=			BoyerMooreSearch(text, pattern)
lowercase    :   Union[str, Any]						=			bms.bad_character_heuristic()

if len(positions) == 0:
		print('No match found')
else:
		print('Pattern found in following positions: ')
		print(positions)